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Imaging perfusion changes in oncological clinical applications by hyperspectral imaging: a literature review

Radiology and Oncology
Volume 56 (2022): Issue 4 (December 2022)
By:
Open Access
|Dec 2022

Figures & Tables

Figure 1

Structure and composition of hyperspectral images and physiological parameters derived from the images, which are typically displayed in false color. NIR PI = near-infrared perfusion index; OHI = organ hemoglobin index; StO2 = oxygen saturation of tissue; TWI = tissue water index
Structure and composition of hyperspectral images and physiological parameters derived from the images, which are typically displayed in false color. NIR PI = near-infrared perfusion index; OHI = organ hemoglobin index; StO2 = oxygen saturation of tissue; TWI = tissue water index

Figure 2

Flow diagram of the selection strategy. Taken from Pfahl et al.15 and reprinted with permission from the publisher.
Flow diagram of the selection strategy. Taken from Pfahl et al.15 and reprinted with permission from the publisher.

Figure 3

Images of the kidney depicting the percentage of HbO2 as a function of color. A dark red represents high values while the yellows and greens indicate lower values. Taken from Best et al.34 and reprinted with permission from the publisher.
Images of the kidney depicting the percentage of HbO2 as a function of color. A dark red represents high values while the yellows and greens indicate lower values. Taken from Best et al.34 and reprinted with permission from the publisher.

Figure 4

(A) Red-Green-Blue (RGB) representation of the imaged brain, including normal and tumor tissue. (B) Extraction of blood vessels from hyperspectral images using the spectral angle mapper algorithm (SAM). (C) Tissue classification map generated from hyperspectral images: tumor tissue is red, normal tissue is green, blood vessels are blue, and background is black. Taken from Fabelo et al.38 and reprinted with permission from the publisher.
(A) Red-Green-Blue (RGB) representation of the imaged brain, including normal and tumor tissue. (B) Extraction of blood vessels from hyperspectral images using the spectral angle mapper algorithm (SAM). (C) Tissue classification map generated from hyperspectral images: tumor tissue is red, normal tissue is green, blood vessels are blue, and background is black. Taken from Fabelo et al.38 and reprinted with permission from the publisher.

Figure 5

Comparison of Red-Green-Blue (RGB) images and near-infrared perfusion index (NIR PI) images recorded in a patient with (A, B) and without postoperative anastomotic insufficiency (C, D). Taken from Köhler et al.9 and reprinted with permission from the publisher.
Comparison of Red-Green-Blue (RGB) images and near-infrared perfusion index (NIR PI) images recorded in a patient with (A, B) and without postoperative anastomotic insufficiency (C, D). Taken from Köhler et al.9 and reprinted with permission from the publisher.

Figure 6

Hyperspectral imaging (HSI) acquisition system in the operating room. Hyperspectral images were acquired within a few seconds with physiologic HSI parameters displayed in false colors. Taken from Moulla et al.45 and reprinted with permission from the publisher.
Hyperspectral imaging (HSI) acquisition system in the operating room. Hyperspectral images were acquired within a few seconds with physiologic HSI parameters displayed in false colors. Taken from Moulla et al.45 and reprinted with permission from the publisher.

Figure 7

Usefulness of hyperspectral imaging (HSI) in establishing transection line during colorectal surgery. The Red-Green-Blue (RGB) image (A) and StO2 map (B) show a patient in whom the clinical transection line (continuous line in black) and HSI transection line (dotted line in blue) were aligned; (C) and (D) show the RGB image and StO2 map, respectively, of a patient in whom the clinical transection line deviated from the HSI transection line. Taken from Barberio et al.51 and reprinted with permission from the publisher.
Usefulness of hyperspectral imaging (HSI) in establishing transection line during colorectal surgery. The Red-Green-Blue (RGB) image (A) and StO2 map (B) show a patient in whom the clinical transection line (continuous line in black) and HSI transection line (dotted line in blue) were aligned; (C) and (D) show the RGB image and StO2 map, respectively, of a patient in whom the clinical transection line deviated from the HSI transection line. Taken from Barberio et al.51 and reprinted with permission from the publisher.

Included articles reporting the use of hyperspectral imaging (HSI) to quantify perfusion changes in clinical applications in oncology

ReferenceYear of publicationNumber of patientsOncologic interventionSystemAlgorithm
Kidneys
Best34 Eye201326Partial nephrectomyDLP HSI, 520–645 nmSupervised multivariate least squares regression
Rose35 Breasts20188Radiation retinopathyTunable laser, 520–620 nm with 5 nm stepsPHYSPEC software (Photon etc., Montreal, QC, Canada)
Chin36201743Skin response to radiationOxyVu-2TM (Hypermed, Inc., Waltham, MA), 500–600 nmThe OxyVu-2TM software (Hypermed, Inc., Waltham, MA)
Pruimboom8202210Mastectomy skin flap necrosisTIVITA™ (Diaspective Vision GmbH, Am Salzhaff, Germany), 500– 1000 nm with 5 nm stepTIVITA™ (Diaspective Vision GmbH, Am Salzhaff, Germany)
Brain
Fabelo37201822Craniotomy for resection of intraaxial brain tumorHyperspec VNIR A-Series (HeadWall Photonics, Massachusetts, USA), 400–1000 nmSpectral angle mapper
Fabelo3820185Craniotomy for resection of intraaxial brain tumor; all 5 patients with grade IV glioblastomaAs in Fabelo37As in Fabelo37
Fabelo3920196Craniotomy for resection of intra-axial brain tumor; all 6 patients with grade IV glioblastomaAs in Fabelo37As in Fabelo37
Fabelo40201922Craniotomy for resection of intraaxial brain tumorAs in Fabelo37As in Fabelo37
Entire GI tract
Jansen-Winkeln41 [Article in German]201847Gastrointestinal surgery with esophageal, gastric, pancreatic, small bowel or colorectal anastomosesAs in Pruimboom8As in Pruimboom8
Upper GI tract
Kohler9201922Hybrid or open esophagectomy followed by reconstruction of gastric conduitAs in Pruimboom8As in Pruimboom8
Moulla42 [Article in German]2020 Video presentation of hybrid esophagectomyAs in Pruimboom8As in Pruimboom8
Schwandner4320204Hybrid esophagectomy followed by reconstructing gastric conduitAs in Pruimboom8As in Pruimboom8
Hennig44202113Hybrid esophagectomy followed by reconstructing gastric conduitAs in Pruimboom8As in Pruimboom8
Moulla45202120PancreatoduodenectomyAs in Pruimboom8As in Pruimboom8
Lower GI tract
Jansen-Winkeln46201924Colorectal resectionAs in Pruimboom8As in Pruimboom8
Jansen-Winkeln47202032Colorectal resectionAs in Pruimboom8As in Pruimboom8
Pfahl482022128Colorectal resectionAs in Pruimboom8As in Pruimboom8
Jansen-Winkeln49202154Colorectal resectionAs in Pruimboom8As in Pruimboom8
Jansen-Winkeln502022115Colorectal resectionAs in Pruimboom8As in Pruimboom8
Barberio51202252Colorectal resectionAs in Pruimboom8As in Pruimboom8
DOI: https://doi.org/10.2478/raon-2022-0051 | Journal eISSN: 1581-3207 | Journal ISSN: 1318-2099
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Language: English
Page range: 420 - 429
Submitted on: Oct 27, 2022
Accepted on: Nov 2, 2022
Published on: Dec 13, 2022
Published by: Association of Radiology and Oncology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
hyperspectral imaging,
oncology,
resection,
perfusion,
cancer
Related subjects:
Medicine,
Clinical medicine,
Internal medicine,
Haematology, oncology,
Radiology

© 2022 Rok Hren, Gregor Sersa, Urban Simoncic, Matija Milanic, published by Association of Radiology and Oncology
This work is licensed under the Creative Commons Attribution 4.0 License.

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